Controls of streamflow generation in small catchments across the snow–rain transition in the Southern Sierra Nevada, California

Abstract

Processes controlling streamflow generation were determined using geochemical tracers for water years 2004–2007 at eight headwater catchments at the Kings River Experimental Watersheds in southern Sierra Nevada. Four catchments are snow‐dominated, and four receive a mix of rain and snow. Results of diagnostic tools of mixing models indicate that Ca2+, Mg2+, K+ and Cl− behaved conservatively in the streamflow at all catchments, reflecting mixing of three endmembers. Using endmember mixing analysis, the endmembers were determined to be snowmelt runoff (including rain on snow), subsurface flow and fall storm runoff. In seven of the eight catchments, streamflow was dominated by subsurface flow, with an average relative contribution (% of streamflow discharge) greater than 60%. Snowmelt runoff contributed less than 40%, and fall storm runoff less than 7% on average. Streamflow peaked 2–4 weeks earlier at mixed rain–snow than snow‐dominated catchments, but relative endmember contributions were not significantly different between the two groups of catchments. Both soil water in the unsaturated zone and regional groundwater were not significant contributors to streamflow. The contributions of snowmelt runoff and subsurface flow, when expressed as discharge, were linearly correlated with streamflow discharge (R2 of 0.85–0.99). These results suggest that subsurface flow is generated from the soil–bedrock interface through preferential pathways and is not very sensitive to snow–rain proportions. Thus, a declining of the snow–rain ratio under a warming climate should not systematically affect the processes controlling the streamflow generation at these catchments. Copyright © 2012 John Wiley & Sons, Ltd.

Seasonal Accumulation and Depletion of Local Sediment Stores of Four Headwater Catchments

“…If precipitation at the nearby sites did not accompany a snow-depth increase at the study sites, that precipitation was assumed to be rain. Snow-density data from nearby US Bureau of Reclamation snow-pillow sites were used to compute snow water equivalent (SWE) for the stream and meteorological station locations according to the method outlined in Liu et al [26]. The Blue Canyon station was used for the northern site and a combination of the Chilkoot Meadow and Poison Ridge stations for the southern site.…”

Effects of Model Spatial Resolution on Ecohydrologic Predictions and Their Sensitivity to Inter-Annual Climate Variability

“…NSE and LNSE for B201 are more sensitive to DEM resolution compared to the other watersheds. Other research [33] suggested that B201 has a smaller subsurface flow component than other Bull sites, and the authors hypothesized that the bedrock geomorphology at B201 may be different from the other watersheds. These results suggest that the model accuracy of streamflow depends on how well surface topography at different resolutions emulates the subsurface topography for individual watersheds.…”

Spatial and Temporal Variability in Baseflow in the Mattole River Headwaters, California, USA

“…Conditions at the soil/bedrock or saprolite/bedrock contact may be influential in determining storage capacity. 25 Permeable bedrock, or the depth of weathered bedrock or saprolite may constitute the primary reservoir of storage for baseflow generation (Liu et al 2013, Price 2011, Rempe and Dietrich 2014, Salve et al 2012, Sayama et al 2011. While soil depths are typically assumed to be greater on lower gradient slopes and in valley fill (Price 2011), studies of the depth of the hydrologically active weathered bedrock zones in argillite and granite have found increasing depth with higher slope position (Holbrook et al 2014, Rempe and Dietrich 2014, Salve et al 2012.…”

“…Water that contributes to baseflow may move 5 through larger pores and preferential flow pathways deep into the saprolite, out of the reach of vegetation except where regolith depths are relatively shallow (Liu et al 2013, Salve et al 2012. Transpiration by riparian vegetation may be less limited by water availability and thus have a pronounced effect on baseflows (Bernal and Sabater 2012, Bren 1997, Hicks et al 1991, Moore et al 2011, Reid 2012, Richard et al 2013).…”

“…Correlation coefficients were calculated between three streamflow metrics ( Table 2) and basin characteristics selected based on their potential influence on baseflow volume and timing , Liu et al 2013, McGuire et al 20 2005, Price 2011, Price et al 2011, Smakhatin 2001, Tetzlaff et al 2009). All basin characteristics shown in Table 3 were calculated in the geographic information systems program QGIS (QGIS Development Team 2013), using modules available in the System for an Automated Geographical Analysis (SAGA) processing toolbox contained within QGIS (Böhner et al 2008).…”